Writing Assignment - Model in Action Essay Example | Topics and Well Written Essays - 500 words

Reading skill Essay Essay Example

Perusing ability Essay Foregrounding or Underscoring Key Ideas When you feature or underline cardinal words and considerations. you are putting the majority of import pieces of the content. There’s an of import achievement at work here: You can’t feature or underline everything. so you need to isolate between the realities and considerations that are a large portion of import ( significant musings ) and those realities and contemplations that are useful however non so of import ( minor or back uping contemplations ) . Feature just the significant musings. so you don’t plug up with a book that’s completely featured. An effectively featured book will accomplish for a simple and productive reappraisal. At the point when you hop back. you’ll be We will compose a custom paper test on Reading expertise Essay explicitly for you for just $16.38 $13.9/page Request now We will compose a custom article test on Reading aptitude Essay explicitly for you FOR ONLY $16.38 $13.9/page Recruit Writer We will compose a custom paper test on Reading ability Essay explicitly for you FOR ONLY $16.38 $13.9/page Recruit Writer quickly helped to remember the considerations that are the greater part of import to recover. Featuring or underscoring significant focuses as you read other than permits you to hold more data from the content. Skim in front and jump back. Increase the content. Make speci? hundred perceptions about the content. Planing Ahead and Jumping Back Skimming in front empowers you to see what’s coming up in your perusing. Page through the content you’re going to peruse. Notice how the content is separated. what the main subjects are. what's more, the request wherein they are secured. Notice cardinal words and contemplations that are undeniable. bulleted. boxed. or then again in any case featured. Planing through the content previously will fix you for what you are going to peruse. It’s a clump like watch intoing out the slopes and bends in the class before a crosscountry race. On the off chance that you know what’s in front. you know how to stride yourself. so you’re arranged to oversee what’s to come. When you? nish your perusing. jump back. Audit the entirety ups. headers. furthermore, featured data in the content. Notice both what the author featured and what you featured. By jumping back. you help harden in your mind the considerations and data you simply read. You’re helped to remember how each idea? T into the entirety. how thoughts and data are associated. At the point when you make associations between musings. you’re significantly more prone to recover them. Surrounding Unfamiliar Words One of the a large portion of import wonts to create is that of revolving around and looking into new words and expressions. On the off chance that conceivable. don’t plunk down to peruse without a word reference close by. It is non unprecedented for the hugeness of a full sentence to depend on the noteworthiness of an individual word or expression. what's more, in the event that you don’t cognize what that word or expression organizations. you won’t comprehend the sentence. Other than. this wont empowers you to quickly and consistently spread out your jargon. so you’ll be an all the more con? gouge peruser and talker. In the event that you don’t hold a word reference promptly accessible. endeavor to discover the hugeness of the word as well as can be expected from its contextâ€that is. the words and contemplations around it. ( There’s more regarding this matter in Lesson 3. ) Then. do certain you look into the word each piece in a matter of seconds as could be expected under the circumstances so you’re certain about its importance. Taging Up the Text Marking up the content makes a direct physical nexus among you and the words you’re perusing. It drives you to pay nearer taking care of the words you read and takes you to a higher level of cognizance. Utilize these three plans to tag up content: ten †HOW TO USE THIS BOOK †Making Marginal Notes Recording your requests and responses in the fringes diverts you from a dormant getting arrangement of data into a functioning member in a duologue. ( If you’re perusing a library book. make your responses in a note pad. ) You will gain considerably more out of the contemplations and data you read about in the event that you make a â€Å"conversation† with the writer. Here are a few representations of the sorts of responses you may create down in the fringe or in your note pad:  ¦ Making Observations Good perusers realize that creators utilize a wide range of plans to show their musings. Regardless of whether you know tiny about those plans. you can do utile perceptions about what you read to more readily comprehend and recover the author’s considerations. You can identify. for representation. the author’s pick of words ; the development of the sentences and sections ; any rehash of words or considerations ; of import inside informations about individuals. topographic focuses. what's more, things ;, etc. This stepâ€making observationsâ€is basic in light of the fact that your perceptions ( what you notice ) lead you to intelligent illations about what you read. Deductions are choices dependent on ground. reality. or on the other hand grounds. You are constantly doing illations dependent on your perceptions. in any event, when you’re non perusing. For representation. on the off chance that you notice that the sky is loaded with dim. substantial mists. you may conclude that it is making a trip to rain ; on the off chance that you notice that your colleague has a pile of planting books around her work area. you may derive that she prefers cultivation. On the off chance that you misconstrue what you read. it is as often as possible since you haven’t looked carefully satisfactory at the content. As a result. you base your illations on your ain contemplations and encounters. non on what’s truly written in the content. You wind up pressuring your ain considerations on the essayist ( rather than tuning in to what the author needs to state ) thus sorting out your ain musings about it. It’s basic. so. that you start to genuinely pay taking care of what creators state and how they state it. In the event that any of this sounds jumbling now. don’t concern. Every one of these contemplations will be thoroughly clarified in the exercises that follow. In the meantime. get down rehearsing dynamic perusing decently well. Get somewhere near taking the pretest. Questions every now and again come up when you read. They might be addressed thusly in the content. yet, by that cut. you may hold overlooked the request! Furthermore, if your request isn’t replied. you may want to talk it with individual: â€Å"Why does the creator depict the new open help approach as ‘unfair’ ? † or â€Å"Why does the character respond as such? † Agreements and discords with the author will undoubtedly begin if you’re effectively perusing. Record them: â€Å"That’s non needfully evident! † or â€Å"This approach sounds good to me. † Connections you note can be either between the content and something that you read before or between the content and your ain experience. For representation. â€Å"I recall encountering a similar way when I. . . † or â€Å"This is like what occurred in China. † Evaluations are your way of keeping up the author legit. In the event that you think the author isn’t providing suf? cient support for what the person is expressing or that there’s something wrong with that help. say as much: â€Å"He says the dropping of the bomb was inescapable. in any case, he doesn’t clarify why† or â€Å"This is a truly sel? sh ground. † eleven READING COMPREHENSION SUCCESS IN 20 MINUTES A DAY Pretest B efore you start your study of understanding achievements. you may want to get an idea of the amount you as of now cognize and the amount you have to larn. On the off chance that that’s the case. take the pretest that follows. The pretest comprises of 50 numerous decision requests covering all the exercises in this book. Normally. 50 requests can’t screen each individual develop or plan you will larn by working through this book. So regardless of whether you get all the requests on the pretest right. it’s about ensured that you will? nd a couple of musings or perusing strategies in this book you didn’t definitely know. On the different manus. in the event that you get numerous requests wrong on this pretest. don’t distress. This book will demo you how to peruse all the more practically. measure by measure. You ought to use this pretest to get a general idea of the amount you definitely know. On the off chance that you get a good grade. you might have the option to pass less clasp with this book than you initially arranged. In the event that you get a bad grade. you may? nd that you will require more than 20 proceedingss a twenty-four hours to obtain through every part and better your understanding achievements. There’s an answer sheet you can use for? lling in the correct answers on page 3. Or on the other hand. on the off chance that you like. simply circle the answer Numberss in this book. On the off chance that the book doesn’t have a place with you. make the Numberss 1â€50 on a bit of paper and enter your answers at that place. Take as much clasp as you have to make this short preliminary. When you? nish. investigate your answers against the answer key at the terminal of this exercise. Each answer offers the exercise ( s ) in this book encourages you about the perusing plan in that request. 1 †LEARNINGEXPRESS ANSWER SHEET †1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. an a B B degree Celsius degree Celsius c hundred degree Celsiuss c hundred degree Celsiuss c hundred degree Celsiuss c hundred degree Celsiuss c hundred degree Celsius nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D nutrient D 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. an a B B 3 degree Celsius degree Celsius c hundred degree Celsiuss c hundred degree Celsiuss c hundred degree Celsiuss c hundred degree Celsiuss c hundred degree Celsius vitam

ThoughtSpot

ThoughtSpot INTRODUCTIONMartin: Hi, today we’re in Palo Alto in the ThoughtSpot office. Hi, Ajeet. Who are you and what are you doing?Ajeet: My name is Ajeet Singh and I’m co-founder and CEO of ThoughSpot. We started this company in 2012, I’ve been in the Valley for about seven years, actually eight years now. I came here in 2007 and joined Oracle for a start-up and started a company called Nutanix in 2009 then I started ThoughSpot in 2012.Martin: How did you come up with that business idea of ThoughtSpot?Ajeet: The business idea of ThoughtSpot was primarily I would say driven by the experience I had at Oracle and then the first startup where I worked on Aster Data Systems that was in 2007-2008 and I saw that the data infrastructure was becoming very scalable, we call it big data now. People were coming up with more scalable model of storing lots and lots of data in a cost efficient way but how this data was delivered to the end users that was not changing that was still done using the tra ditional reporting model and thats very painful because it requires a lot of work and experts you need technical experts to build reports for business users. It’s very slow and inefficient process and thats where we saw the opportunity.We saw that in the consumer space there are two billion people using search and the way to find information and we thought what if we could actually provide that user experience for numbers? What if marketing manager would go to a simple search bar and ask for revenue in a particular quarter for a particular campaign they might have done on a product line or whatever they might be interested in and just get the answers on the fly without actually having to go to somebody to say: Hey, can you brief me a report? And go back and forth on what the report should be like, changing it becomes very painful and those kinds of things.We saw there being a huge difference in how people were accessing information at home through Google and Facebook and Linkedin and at work through traditional business intelligence reporting technology and thats the gap we’re trying to fill.Martin: How did you start about it? How long did it take for you to build the first iteration of the product? When did you raise money? When did you talk to first customers?Ajeet: I would say we talked to first prospects even before we started the company. So early 2012 we spent time just learning the market. We realized that this is a big market and theres a big problem.So my way of thinking about startup has been driven more by markets. Picking the right market first is the first matter for me has been the most important thing, the idea actually comes later. What is the market and what is the problem that we can solve? There can be various ways of solving it. But before I get down identifying how exactly we want to solve the problem I want to spend time with customers, I want to look at competition, Im going to look at partners and understand how we might sell a prod uct to them, how people will buy my product in those markets and things like that.So we spent about six months just studying the market and talking to prospects, talking to partners talking to be people who sell to these companies, just learning. And once we figured that out then we learned about the problem obviously and then we went to the white board and we said, What would be the most awesome way of solving this problem? The most simple way? Because technology has become very complex and lot of innovation now is just bringing simplicity to complex way of doing things because complexity means you’re going to be slow and it will be very expensive to do things. Make them simple, lot of people can do those tasks and then do it more efficiently and they can do it easily.So we came up with this idea that if you apply search to analytics it can actually be 10X to 100X difference in how people operate today. Then we started defining the product, defining our architecture of the produc t and spent about six months on designing the product. When I say design I’m talking about the technical design, some part of UI design. Because what we’re building is very UX driven but in the back there is a lot of complexity that we have to deal with. So it was important to make sure that all platform is architectured well so when we go to market we dont have to go iterate and iterate.Martin: At what point in time did you think about talking to investors?Ajeet: So this being my second startup. I already had some existing relationships with some investors. As I said my previous company I started 2009 and that company has actually done really well so in the process I had the opportunity to work with Lightspeed Ventures and Khosla Ventures and both of them are great investors great people. For me its only been about people because the kinds of things we do for some people it might look crazy. Only thing that is guaranteed is the experience of working with awesome people. So it w as basically working with the same sort of folks, making me sure that we are going after the market and a problem that is highly valuable, we spent time on that and raising money itself was I would say not the biggest challenge that we had to overcome.BUSINESS MODEL OF THOUGHTSPOTMartin: Ajeet, let’s talk about the business model of ThoughtSpot. So what are basically the customers that you are targeting and especially in terms of the functions that maybe more adoptive to your solutions? And exactly what is the value proposition besides only being simpler to get access to data and analytics that you deliver to those customers?Ajeet: We currently are focused on mid to large enterprises (Global 2000), that is the way I would categorize my market segments and within these we are building a solution that is designed for business functions (sales, marketing, operations, supply chain, finance, and so forth) but what were building is very scalable. So we can go to a large company and pote ntially they might have 50,000 people that would want to use ThoughtSpot. So this is a solution that can start small with a few hundred people and then on time you can scale to thousands of people.So we do also get involved with IT departments, so typically a director or VP of Business Intelligence would be someone we will to talk to, or somebody from the business side. And the value proposition for them is imagine you were talking to marketing person, a head of marketing, whois primary responsibility is to reduce customer churn, then they have currently existing way of looking at data of how the churn numbers are over time and how different campaigns might have an effect on churn. We would go in and demonstrate to them with our technology they can get access to their churn data in a much more granular and much more ad hoc way, because theyre launching campaigns and this is actually a real example. Previously it would take them two to three months to find out what the effect of the campaign was on their churn numbers, now they can do that in a few minutes. So launching campaigns and next day as soon as the data starts to come in I can go in and understand how major numbers are changing by geography by product line on those kinds of things and then based on that I can then optimize which campaigns I do more, which campaigns I do less, and things like that.We now have customers that are across almost all major industry verticals (retail, financial services, telecom, manufacturing, and so on), so we work with a whole range of people.Martin: From my point of view the major benefit of ThoughtSpot is that you’re making it super easy and accessible for people to access the data in a company. How does a customer of you ingest the data into ThoughtSpot and how do you set a specific user rights to data because maybe only the c-level management would like to have access to financials and some other people should not?Ajeet: So were doing to data / to enterprise data wha t Google did to newspapers. Long time ago people used to get information from newspapers and it was published by a few people and it was then formatted, it had headlines and there’s a front-page and sports page and so on. It was very rigid in structure because only a few people could publish the newspaper. But with Google now anybody can go in and find information they want and the news they want and things like that.When we apply the same model to enterprise the user experience model remains the same but other things change. The kind of things that changed I would say number one is the type of data we’re going after. Google and most of search engines that are out there, the data they’re ingesting are documents, there are web pages, radios, videos and things like that. What we’re going after is, we are connecting to the most valuable data assets in large companies. So that data sits in their ERP system or data ware houses or any kind of databases, now we have Hadoop, it’s complicated environment.So the type of data we have to deal with is very complicated, so we had to build a new kind of search technology that can understand all the complexities in the databases like tables and joints, columns and things like that. And it was done from scratch.Now when youll use, search and analyze this data the performance expectations are also completely changed because in our traditional reporting environment your report comes back in 20-30 seconds and that’s considered fast. But if you’re in the search bar, the user expectation is very different. So our search engine is backed by a very high performance computational engine that can take data from hundreds of tables and billions of rows and do joints on the fly and give results to end users at certain speed.The third big change is security as you said. People want to get enough data across departments, across different levels and things of that nature.And last but not the least, I will say the biggest way in which we differ from a classic search engine is accuracy and trustworthiness of the data. Because if I am looking for friends on Facebook or im looking for books on Amazon, or Im looking for coffee shops on Google Ill get probably 10, 20, 30 results for any given search where I’ll have to pick which of them is relevant for me. There is no guarantee that something is going to give me an answer which Im interested in, but if I’m giving ThoughtSpot in the hands of a hundred marketing analysts or hundred financial analysts or thousands of people in an organization that might be doing very specific functions, they are used to getting data from reports or Excel sheets. They will not accept guesswork.So we have to build the technology that does not do any guesswork, it actually uses intelligence that is already there their data and uses that combined with the inputs from the user through the search bar and always gives them one single result. Even though on the front it’s very simpl e, in the backend we had to handle scale and complexity of data and we have to handle security and governance. And the nice thing is that when we go to our customers with ThoughtSpot the business users are happy because all of them can access data on the fly whenever they want to. IT department is also happy because today IT department is buried under a long list of report requests and theyre always behind. Some of them told us they feel like report monkeys and they build more and more reports as opposed to going to the business and saying what kind of data can I give you so that you can make better business decisions.With ThougtSpot IT can be in the business of finding new data sources, provisioning new data sources and putting security and governance around them and business users can access the data that they’re supposed to access.Martin: Ajeet, how did you acquire the first one or two customers? How do you convince them: Guys, please give me access to the most precious data th at you have and I give you some kind of answers to that?Ajeet: In the Valley and elsewhere as well there is a model of working with your investors to get access to initial customers. We also do that and it is extremely helpful in making introductions to potential customers. But at least for me the way we like to build the company is it has to be done in an organic way and what I mean by that is we have to find a natural fit between what we are building and the segment of the market that will be interested in.When Im going through the process of defining and building the product the exact specific market segment is not that well known and also how I place the product to them and how they will they use it, what’s important and what’s not that also is not known. So you go through this process of finding the product market fit. The product market fit is better if it is done organically by the company itself where you might have I would say 18 months before we had a product we had an inside sales person who was reaching out to people, calling them and doing demos. About a year before having a product, we had a full sales team and these are expensive sales teams, you can spend anywhere from half a million to a million every year on sales team. But for the kinds of things we are doing it typically takes 12 months to figure out what exactly is the product market fit. Is my product good for small companies or is it good for big companies? If it is good for big companies who are the people that I need to talk to? Who will be more in the buying process and what do they look for? What features are important? All this has to be discovered in the field organically.If you just depend on your own network which is friends and family, or you depend on investors then youll go to people who will try your product because there was an introduction that was made or they are your friends. And that might give you false positive because they would say: This is awesome, this is grea t. I love it. You dont know if theyre saying that because they love you or they love the product. They can also be false negative because maybe theyre not the right users for the product that you are building the right user is somewhere else.So I think it has to be a good mix of reaching out to the network as well as organic outreach with a bias I have a personal bias towards organic outreach. And in our case this is what we did, we reached out to people organically, we showed them the product, we got their feedback and where the best fit we went after those. Since we were building enterprise product and the data is very sensitive (that’s what you were hinting at), it is on-premise product so we’re actually going to deploy our product inside the firewalls of a customer so that they dont have to worry about data security and those kind of things.Martin: Did provide your first customers just free trial to show the value and then after youve shown the value then you said: Okay, let s put some revenue numbers behind that? Or did you start in the beginning: I would like to put some dollar sign on that?Ajeet: Yes, even our beta were paid, our first beta were also paid. We didnt want to give our product out for free even for trial because it leads to the same situation I was talking about earlier. You end up with people that just want to kick the trials either because you know they have fun keeping trials with new technology or taking it because theyre doing a favor to you and your investors. But if you ask for even $1, asking for $1 is so much more valuable than giving your product away for free. So the goal there is not to make money and fund your business, the goal is to make sure that the problem youre trying to solve is a real problem.So if I go to a business and it is lets say an accounting firm that wants to understand how should I do all my business and which of my customers are the most profitable for me that has to be a real problem so that I know you if the product actually works there is product promise that has to match with the business problem and then value can get created. So its very important to make sure that youre going after real problems and not just trying to to get lucky or have some good accidents along the way and eventually get to success.Martin: Ajeet, lets talk about the technology of ThoughtSpot. If I’m a potential client and Im signing up to ThoughtSpot, how does it work then? How is your machine learning working?Ajeet: The way we thought about building the product was we want make it simple on the frontend for the end users but we also want to make it simple on the backend for IT to set it up. Because traditional products theyre very clunky for business users to end users we call them humans so we like to call ourselves analytics for humans, average human beings might be experts in sales marketing but not in analytics should be able to access data. So you want to make it simple for the humans on the fronten d but on the backend also there is a lot of work that is done to set up BI products. You have to kind of enter the data source obviously, then you have to talk to the business users and say: Which reports do you want? And for those reports I have to identify where the data will come from, what kind of data model Im going to need, if the query is going very slow I might have to build what is known as cubes (it’s basically pre-aggregation).I would do those computations over night, so next morning you can look at your revenue for each country. But if you wanted to look at revenue by each product line: Oops, I didn’t do that. Thats a problem with that. So you have to define all these things in advance.With ThoughtSpot we leverage lot of memory computing at scale and we have cut down on any sort of pre-computation that is required and every step we tried to make things simple and cut down on any human intervention that might be required. So what we do is we will go to your signing up with ThoughtSpot, we would implement our product, install our product in your data center. That is typically done within a day and we will get you live within 2-4 weeks from day zero. And thats just to give you a sense. The classic BI deployment takes about six months, so we have shrunk that time of six months down to 2-4 weeks for some of the biggest and most complicated data sets that are around there.The way we do this is we connect to data sources and we then build sort of a mirror image of the scheme of these data sources. And then our search engine, as I was saying earlier, has been built to understand all kinds of things that in our scheme, it will understand all the tables and joints and lots of meta data that is already present.A lot of times customers say: My data is dirty and it is not going to be ready for this, which is all data of the world is dirty and our system assumes that the data is going to be dirty and install into their dirty data. But we dont expect you to s ort of massage data in a certain way, or create a particular very specific kind of schema and set it up nicely. If you want to sort of apply a cleaning filter on top of your data so you might get very dirty data and then we allow you to put a simple filter which is a matter of hours and then you can define that and then expose that to your end users. And we will basically go to filter so all the dirtiness is filtered out and we are presenting clean results to end users.So the technology that we have built, the search engine, that leverages all the investments you have made in your data sources already to cut down that time and we dont have to go through the long process of defining any kind of natural language models or any machine learning models and then test them,verify them, and over time we get better thats not the model. On day one we are good, over time it become great.Martin: Ajeet, how did you come up with the revenue model and what is actually driving the pricing? So is i t more like storage, or computational power, or the number of users, or the just companies size? What is driving the revenue?Ajeet: Yes, it’s a good question because if you look at the pricing model that exists in the market today its a per user model. So if you’re a customer and you’re signing up I would ask you: How many users do you think you will have? And you say 100 and Ill try to sell you license for 200. And he’ll say: Give me 150. Lets started with 150 and will see over time where we end.And then IT Department says: We have 150 licenses for this. First of all, since the technology is complex lot of those licenses go waste. And if more people want to access to information, let’s say you go to new department which has 1000 people, then you need to go back to the vendor and again ask them to spend more money with them.The whole idea behind ThoughtSpot is adoption of data. Because today only 22% of people can access data and we want to increase that number significant ly. And for each of those people, we want them to be able to access the data very-very frequently, so we are talking about 10x to 100x increasing adoption of data.So we dont want to penalize our customers on number of users, we do not charge based on the number of users. Even our smallest product skill that will sell to you, you can put unlimited number of users on that. How we price our product is we set up lines and its building block models, Lego block model. So you can drag and stack them as much as you need to scale and you can start small and grow over time, but we do not have any limit on number of users for these appliances.Martin: Is this then query based or data storage based?Ajeet: It’s based on appliance which can find amount of data.ADVICE TO ENTREPRENEURS FROM AJEET SINGH In Palo Alto (CA), we meet Co-Founder CEO of ThoughtSpot, Ajeet Singh. Ajeet talks about his story how he came up with the idea and founded ThoughtSpot how the current business model works, as well as he provides some advice for young entrepreneurs.INTRODUCTIONMartin: Hi, today we’re in Palo Alto in the ThoughtSpot office. Hi, Ajeet. Who are you and what are you doing?Ajeet: My name is Ajeet Singh and I’m co-founder and CEO of ThoughSpot. We started this company in 2012, I’ve been in the Valley for about seven years, actually eight years now. I came here in 2007 and joined Oracle for a start-up and started a company called Nutanix in 2009 then I started ThoughSpot in 2012.Martin: How did you come up with that business idea of ThoughtSpot?Ajeet: The business idea of ThoughtSpot was primarily I would say driven by the experience I had at Oracle and then the first startup where I worked on Aster Data Systems that was in 2007-2008 and I saw that the data infrastructure was becomin g very scalable, we call it big data now. People were coming up with more scalable model of storing lots and lots of data in a cost efficient way but how this data was delivered to the end users that was not changing that was still done using the traditional reporting model and thats very painful because it requires a lot of work and experts you need technical experts to build reports for business users. It’s very slow and inefficient process and thats where we saw the opportunity.We saw that in the consumer space there are two billion people using search and the way to find information and we thought what if we could actually provide that user experience for numbers? What if marketing manager would go to a simple search bar and ask for revenue in a particular quarter for a particular campaign they might have done on a product line or whatever they might be interested in and just get the answers on the fly without actually having to go to somebody to say: Hey, can you brief me a r eport? And go back and forth on what the report should be like, changing it becomes very painful and those kinds of things.We saw there being a huge difference in how people were accessing information at home through Google and Facebook and Linkedin and at work through traditional business intelligence reporting technology and thats the gap we’re trying to fill.Martin: How did you start about it? How long did it take for you to build the first iteration of the product? When did you raise money? When did you talk to first customers?Ajeet: I would say we talked to first prospects even before we started the company. So early 2012 we spent time just learning the market. We realized that this is a big market and theres a big problem.So my way of thinking about startup has been driven more by markets. Picking the right market first is the first matter for me has been the most important thing, the idea actually comes later. What is the market and what is the problem that we can solve? Th ere can be various ways of solving it. But before I get down identifying how exactly we want to solve the problem I want to spend time with customers, I want to look at competition, Im going to look at partners and understand how we might sell a product to them, how people will buy my product in those markets and things like that.So we spent about six months just studying the market and talking to prospects, talking to partners talking to be people who sell to these companies, just learning. And once we figured that out then we learned about the problem obviously and then we went to the white board and we said, What would be the most awesome way of solving this problem? The most simple way? Because technology has become very complex and lot of innovation now is just bringing simplicity to complex way of doing things because complexity means you’re going to be slow and it will be very expensive to do things. Make them simple, lot of people can do those tasks and then do it more eff iciently and they can do it easily.So we came up with this idea that if you apply search to analytics it can actually be 10X to 100X difference in how people operate today. Then we started defining the product, defining our architecture of the product and spent about six months on designing the product. When I say design I’m talking about the technical design, some part of UI design. Because what we’re building is very UX driven but in the back there is a lot of complexity that we have to deal with. So it was important to make sure that all platform is architectured well so when we go to market we dont have to go iterate and iterate.Martin: At what point in time did you think about talking to investors?Ajeet: So this being my second startup. I already had some existing relationships with some investors. As I said my previous company I started 2009 and that company has actually done really well so in the process I had the opportunity to work with Lightspeed Ventures and Khosla Ve ntures and both of them are great investors great people. For me its only been about people because the kinds of things we do for some people it might look crazy. Only thing that is guaranteed is the experience of working with awesome people. So it was basically working with the same sort of folks, making me sure that we are going after the market and a problem that is highly valuable, we spent time on that and raising money itself was I would say not the biggest challenge that we had to overcome.BUSINESS MODEL OF THOUGHTSPOTMartin: Ajeet, let’s talk about the business model of ThoughtSpot. So what are basically the customers that you are targeting and especially in terms of the functions that maybe more adoptive to your solutions? And exactly what is the value proposition besides only being simpler to get access to data and analytics that you deliver to those customers?Ajeet: We currently are focused on mid to large enterprises (Global 2000), that is the way I would categorize my market segments and within these we are building a solution that is designed for business functions (sales, marketing, operations, supply chain, finance, and so forth) but what were building is very scalable. So we can go to a large company and potentially they might have 50,000 people that would want to use ThoughtSpot. So this is a solution that can start small with a few hundred people and then on time you can scale to thousands of people.So we do also get involved with IT departments, so typically a director or VP of Business Intelligence would be someone we will to talk to, or somebody from the business side. And the value proposition for them is imagine you were talking to marketing person, a head of marketing, whois primary responsibility is to reduce customer churn, then they have currently existing way of looking at data of how the churn numbers are over time and how different campaigns might have an effect on churn. We would go in and demonstrate to them with our technolo gy they can get access to their churn data in a much more granular and much more ad hoc way, because theyre launching campaigns and this is actually a real example. Previously it would take them two to three months to find out what the effect of the campaign was on their churn numbers, now they can do that in a few minutes. So launching campaigns and next day as soon as the data starts to come in I can go in and understand how major numbers are changing by geography by product line on those kinds of things and then based on that I can then optimize which campaigns I do more, which campaigns I do less, and things like that.We now have customers that are across almost all major industry verticals (retail, financial services, telecom, manufacturing, and so on), so we work with a whole range of people.Martin: From my point of view the major benefit of ThoughtSpot is that you’re making it super easy and accessible for people to access the data in a company. How does a customer of you i ngest the data into ThoughtSpot and how do you set a specific user rights to data because maybe only the c-level management would like to have access to financials and some other people should not?Ajeet: So were doing to data / to enterprise data what Google did to newspapers. Long time ago people used to get information from newspapers and it was published by a few people and it was then formatted, it had headlines and there’s a front-page and sports page and so on. It was very rigid in structure because only a few people could publish the newspaper. But with Google now anybody can go in and find information they want and the news they want and things like that.When we apply the same model to enterprise the user experience model remains the same but other things change. The kind of things that changed I would say number one is the type of data we’re going after. Google and most of search engines that are out there, the data they’re ingesting are documents, there are web pages , radios, videos and things like that. What we’re going after is, we are connecting to the most valuable data assets in large companies. So that data sits in their ERP system or data ware houses or any kind of databases, now we have Hadoop, it’s complicated environment.So the type of data we have to deal with is very complicated, so we had to build a new kind of search technology that can understand all the complexities in the databases like tables and joints, columns and things like that. And it was done from scratch.Now when youll use, search and analyze this data the performance expectations are also completely changed because in our traditional reporting environment your report comes back in 20-30 seconds and that’s considered fast. But if you’re in the search bar, the user expectation is very different. So our search engine is backed by a very high performance computational engine that can take data from hundreds of tables and billions of rows and do joints on the fly a nd give results to end users at certain speed.The third big change is security as you said. People want to get enough data across departments, across different levels and things of that nature.And last but not the least, I will say the biggest way in which we differ from a classic search engine is accuracy and trustworthiness of the data. Because if I am looking for friends on Facebook or im looking for books on Amazon, or Im looking for coffee shops on Google Ill get probably 10, 20, 30 results for any given search where I’ll have to pick which of them is relevant for me. There is no guarantee that something is going to give me an answer which Im interested in, but if I’m giving ThoughtSpot in the hands of a hundred marketing analysts or hundred financial analysts or thousands of people in an organization that might be doing very specific functions, they are used to getting data from reports or Excel sheets. They will not accept guesswork.So we have to build the technology that does not do any guesswork, it actually uses intelligence that is already there their data and uses that combined with the inputs from the user through the search bar and always gives them one single result. Even though on the front it’s very simple, in the backend we had to handle scale and complexity of data and we have to handle security and governance. And the nice thing is that when we go to our customers with ThoughtSpot the business users are happy because all of them can access data on the fly whenever they want to. IT department is also happy because today IT department is buried under a long list of report requests and theyre always behind. Some of them told us they feel like report monkeys and they build more and more reports as opposed to going to the business and saying what kind of data can I give you so that you can make better business decisions.With ThougtSpot IT can be in the business of finding new data sources, provisioning new data sources and putting security and governance around them and business users can access the data that they’re supposed to access.Martin: Ajeet, how did you acquire the first one or two customers? How do you convince them: Guys, please give me access to the most precious data that you have and I give you some kind of answers to that?Ajeet: In the Valley and elsewhere as well there is a model of working with your investors to get access to initial customers. We also do that and it is extremely helpful in making introductions to potential customers. But at least for me the way we like to build the company is it has to be done in an organic way and what I mean by that is we have to find a natural fit between what we are building and the segment of the market that will be interested in.When Im going through the process of defining and building the product the exact specific market segment is not that well known and also how I place the product to them and how they will they use it, what’s important and what’s n ot that also is not known. So you go through this process of finding the product market fit. The product market fit is better if it is done organically by the company itself where you might have I would say 18 months before we had a product we had an inside sales person who was reaching out to people, calling them and doing demos. About a year before having a product, we had a full sales team and these are expensive sales teams, you can spend anywhere from half a million to a million every year on sales team. But for the kinds of things we are doing it typically takes 12 months to figure out what exactly is the product market fit. Is my product good for small companies or is it good for big companies? If it is good for big companies who are the people that I need to talk to? Who will be more in the buying process and what do they look for? What features are important? All this has to be discovered in the field organically.If you just depend on your own network which is friends and f amily, or you depend on investors then youll go to people who will try your product because there was an introduction that was made or they are your friends. And that might give you false positive because they would say: This is awesome, this is great. I love it. You dont know if theyre saying that because they love you or they love the product. They can also be false negative because maybe theyre not the right users for the product that you are building the right user is somewhere else.So I think it has to be a good mix of reaching out to the network as well as organic outreach with a bias I have a personal bias towards organic outreach. And in our case this is what we did, we reached out to people organically, we showed them the product, we got their feedback and where the best fit we went after those. Since we were building enterprise product and the data is very sensitive (that’s what you were hinting at), it is on-premise product so we’re actually going to deploy our produc t inside the firewalls of a customer so that they dont have to worry about data security and those kind of things.Martin: Did provide your first customers just free trial to show the value and then after youve shown the value then you said: Okay, lets put some revenue numbers behind that? Or did you start in the beginning: I would like to put some dollar sign on that?Ajeet: Yes, even our beta were paid, our first beta were also paid. We didnt want to give our product out for free even for trial because it leads to the same situation I was talking about earlier. You end up with people that just want to kick the trials either because you know they have fun keeping trials with new technology or taking it because theyre doing a favor to you and your investors. But if you ask for even $1, asking for $1 is so much more valuable than giving your product away for free. So the goal there is not to make money and fund your business, the goal is to make sure that the problem youre trying to so lve is a real problem.So if I go to a business and it is lets say an accounting firm that wants to understand how should I do all my business and which of my customers are the most profitable for me that has to be a real problem so that I know you if the product actually works there is product promise that has to match with the business problem and then value can get created. So its very important to make sure that youre going after real problems and not just trying to to get lucky or have some good accidents along the way and eventually get to success.Martin: Ajeet, lets talk about the technology of ThoughtSpot. If I’m a potential client and Im signing up to ThoughtSpot, how does it work then? How is your machine learning working?Ajeet: The way we thought about building the product was we want make it simple on the frontend for the end users but we also want to make it simple on the backend for IT to set it up. Because traditional products theyre very clunky for business users to end users we call them humans so we like to call ourselves analytics for humans, average human beings might be experts in sales marketing but not in analytics should be able to access data. So you want to make it simple for the humans on the frontend but on the backend also there is a lot of work that is done to set up BI products. You have to kind of enter the data source obviously, then you have to talk to the business users and say: Which reports do you want? And for those reports I have to identify where the data will come from, what kind of data model Im going to need, if the query is going very slow I might have to build what is known as cubes (it’s basically pre-aggregation).I would do those computations over night, so next morning you can look at your revenue for each country. But if you wanted to look at revenue by each product line: Oops, I didn’t do that. Thats a problem with that. So you have to define all these things in advance.With ThoughtSpot we leverage lot of memory computing at scale and we have cut down on any sort of pre-computation that is required and every step we tried to make things simple and cut down on any human intervention that might be required. So what we do is we will go to your signing up with ThoughtSpot, we would implement our product, install our product in your data center. That is typically done within a day and we will get you live within 2-4 weeks from day zero. And thats just to give you a sense. The classic BI deployment takes about six months, so we have shrunk that time of six months down to 2-4 weeks for some of the biggest and most complicated data sets that are around there.The way we do this is we connect to data sources and we then build sort of a mirror image of the scheme of these data sources. And then our search engine, as I was saying earlier, has been built to understand all kinds of things that in our scheme, it will understand all the tables and joints and lots of meta data that is already present .A lot of times customers say: My data is dirty and it is not going to be ready for this, which is all data of the world is dirty and our system assumes that the data is going to be dirty and install into their dirty data. But we dont expect you to sort of massage data in a certain way, or create a particular very specific kind of schema and set it up nicely. If you want to sort of apply a cleaning filter on top of your data so you might get very dirty data and then we allow you to put a simple filter which is a matter of hours and then you can define that and then expose that to your end users. And we will basically go to filter so all the dirtiness is filtered out and we are presenting clean results to end users.So the technology that we have built, the search engine, that leverages all the investments you have made in your data sources already to cut down that time and we dont have to go through the long process of defining any kind of natural language models or any machine learn ing models and then test them,verify them, and over time we get better thats not the model. On day one we are good, over time it become great.Martin: Ajeet, how did you come up with the revenue model and what is actually driving the pricing? So is it more like storage, or computational power, or the number of users, or the just companies size? What is driving the revenue?Ajeet: Yes, it’s a good question because if you look at the pricing model that exists in the market today its a per user model. So if you’re a customer and you’re signing up I would ask you: How many users do you think you will have? And you say 100 and Ill try to sell you license for 200. And he’ll say: Give me 150. Lets started with 150 and will see over time where we end.And then IT Department says: We have 150 licenses for this. First of all, since the technology is complex lot of those licenses go waste. And if more people want to access to information, let’s say you go to new department which has 10 00 people, then you need to go back to the vendor and again ask them to spend more money with them.The whole idea behind ThoughtSpot is adoption of data. Because today only 22% of people can access data and we want to increase that number significantly. And for each of those people, we want them to be able to access the data very-very frequently, so we are talking about 10x to 100x increasing adoption of data.So we dont want to penalize our customers on number of users, we do not charge based on the number of users. Even our smallest product skill that will sell to you, you can put unlimited number of users on that. How we price our product is we set up lines and its building block models, Lego block model. So you can drag and stack them as much as you need to scale and you can start small and grow over time, but we do not have any limit on number of users for these appliances.Martin: Is this then query based or data storage based?Ajeet: It’s based on appliance which can find amou nt of data.ADVICE TO ENTREPRENEURS FROM AJEET SINGHMartin: Let’s talk about your advice for first time entrepreneurs. This is your second company. What have you learned along the way where you said this was a major learning for me going forward and I will never forget this lesson?Ajeet: Yes, there are lots of them. If you talk to anybody who has being at the startup at any capacity its the best place to learn. So my number one advice is to find amazing startups and spend some time there either in a full capacity or an employee. Because if youre someone whos driven by passion for creation which is most of people are at least here in the Valley and many other places in the world where large technical talent. So when working at the startup aou see the opportunity to have a much more open playing field where you can have your ideas come to life very quickly.If you are starting as an entrepreneur I like not to be very prescriptive about these things because every situation is very diff erent. But for me I think what is important is understanding the risk model you’re opting into, either directly or indirectly. I like to look at risks in two dimensions: market risk and execution risk. Is there going to be a market for my product, the idea that I have? And that is market risk. And if I am actually successful then i is going to be valuable that’s how I look at market risk. And I also look at the history of companies that have become successful in this market. And if I see a market that has 10-20 billion plus dollar companies built in the last ten-fifteen years that tells me that this market is large and it supports building large independent companies. On the execution side it is Can I actually build the project that Im talking about and sell it? That is the execution risk.So I personally like to go after opportunities that are very low in market risk and very high in execution risk because I don’t want to spend several years of my time and some of other peopl e time and find that there is no market for my product. But we want the execution risk to be high because you want to set a high bar for anybody else to be able to copy what you’re doing.So that is one model and Im not saying that should be the only model. There is a way of building companies that falls in this bucket. There is another model that is high market risk and low execution risk at least to begin with where I dont know if there’s a market. If you think about Uber or something else, its questionable whether market risk is low or high. But lots of consumer startups would come in that bucket.But what you have to focus on is really mitigating market risk before you do much execution. So understand the risk profile you opting into and focus on that part first. If your execution risk is high then mitigate execution risk by thinking through the product you’re going to build and thinking how you will set it, thinking through the architecture of the product before you start c alling. If your market risk is very high get the hell out of your office and go out and understand what the market is like, prototype and understand all the lean startup stuff.Martin: And assume you would have checked the market risk which is low and now the question is if youre checking on the execution risk how do you identify whether you potential solution will be like 10x or 100x better than existing solutions?Ajeet: I think some of that comes from what people call it vision. Its like you understand the market and you understand the problem. Now I was saying early in the interview, the way I like to think of the idea is what is the most awesome way of solving the problem? And the most ideal way if I had infinite money and infinite amount of people, everything? What is the most ideal way of solving this problem? And then go from there, then you start to make it viable and fix the execution risk. So I would say in the beginning it has to be driven by your vision and passion and g ut. There is no sort of science to it, big part of building a company is making gut calls.Martin: Ajeet, thank you so much for sharing your knowledge.Ajeet: Thank you, Martin. Thank you so much for having me.Martin: Great! And if you have a company and you dont know whether your business people are really checking all the data and getting the most out of, check out ThoughtSpot. Thanks.

Marriage and Twelfth Night - 1541 Words

‘At the end of Shakespeare’s comedic plays all complications and disorders are resolved and a new order is generated to the satisfaction of the audience.’ to what extent is this true of Shakespeare’s Twelfth Night? It is easily argued that Shakespeare’s comedic plays have a similar, formulaic, structure. Dr Schwartz from the California Polytechnic State University argues that the ‘action of a comedy traces a movement from conflict to the resolution of conflict’. There are many disorders and complications in each plot, which by the end of the play must be resolved for the satisfaction of the Elizabethan audience, and in some perspectives, this applies to the modern day audience as well. Twelfth Night poses many different arguments as to†¦show more content†¦Antonio is a character whom it could be argued does not receive a happy ending. It is easily identified by particularly modern audiences, that Antonio is a character who may have homosexual desires. The language used in: ‘my desire, more sharp than steel, did spur me on’ could be interpreted as rather lustful especially the words ‘sharper than steel’ which suggests sexual imagery. This ci rcumstance of mistaken identity, when Antonio is in trouble and mistakes Cesario for Sebastian, for some audiences leans more towards tragedy than comedy because the consequences are more severe. We see how betrayed Antonio feels in the line ‘how vile an idol proves his god!’ Antonio worshipped Sebastian like an idol, and ‘relieved him with such sanctity of love’, only to discover he is the ‘beauteous evil’. The audience may feel pity towards Antonio; this is something they will want resolved. It can be argued this does happen when Sebastian returns. This may have been a good enough resolution for an Elizabethan audience, as many of them will have had no desire to see a homosexual pairing, and perhaps Sebastian’s marriage to Olivia would leave them satisfied that no homoerotic acts occur. 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Growth Prospects E Commerce And Strategic Acquisitions Essay

Opportunities are meant to take advantage of once being noticed. Like how Amazon took notice of its data in the early 2000’s, it needs to take notice of other opportunities that are laid before them. The opportunities that they should continue to focus would be growth prospects e-commerce and strategic acquisitions. Growth prospects e-commerce E-commerce has continued to be a business trend and it continued to grow in 2015. According to the data the Stefany Zaroban got from the U.S. Commerce Department, the web sales in 2015 totaled â€Å"$341.7 billion for the year, a 14.6% increase over 2014’s $298.3 billion† (Zaroban, 2016). This reveals that Amazon would continue to have the chance to increase their sales as well as an increase to bringing in more customers. According to the data that GlobalData received, U.S. had around â€Å"300 million internet users in the year 2014 which was an increase of 7% over the previous years† (GlobalData). 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The Input Stage ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · CAGE VRIO Analysis CPM matrix EFE matrix IFE matrix II. The Matching Stage ï‚ · ï‚ · ï‚ · BCG matrix SPACE matrix GSM matrix III. The Decision Stage ï‚ · ï‚ · Matrix Analysis QSPM matrix IV. Questions ï‚ · ï‚ · ï‚ · ï‚ · Assessment of eBay ´s choice of market entry strategy for China, listing both the advantagesRead MoreEbay in China - Case Study4108 Words   |  17 Pages21/11/2011 E-Bay Strategy In China Alliance or Acquisition? Case Study Strategic Management Gabriela Ã…  alamonovà ¡ Barbora Jandovà ¡ Pierrick Boissel Julien Meunier Alexandre Godet SUMMARY I. The Input Stage ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · CAGE VRIO Analysis CPM matrix EFE matrix IFE matrix II. The Matching Stage ï‚ · ï‚ · ï‚ · BCG matrix SPACE matrix GSM matrix III. The Decision Stage ï‚ · ï‚ · Matrix Analysis QSPM matrix IV. Questions ï‚ · ï‚ · ï‚ · ï‚ · Assessment of eBay ´s choice of market entry strategy for China, listing

Life in the Past and Life Now-Comparison. Free Essays

Life in the past and life now-comparison. Over the last century, there have been many significant changes in the way we live. Obviously, it is hard to compare the life of the ancient people and the life of the people of the twenty first century because so many changes have occurred. We will write a custom essay sample on Life in the Past and Life Now-Comparison. or any similar topic only for you Order Now However, even the changes that have taken place over the last ten years are amazing. To start with, people were not able to travel such long distances in such short period of time. The planes were not that popular then because they used to be a very expensive means of transport. Nowadays, we have become very mobile. We have fast and comfortable cars and more and more people also travel by plane. Moreover, in the past people had to work much harder as they did not have tools which made their work easier. Today, most of the difficult and dangerous work is done by computers and other machines. In the past the conditions of living were not as comfortable as they are now. There were not bathrooms and running water in many houses, besides many people could not afford household appliances like fridge, TV set or vacuum cleaner because they used to be luxurious goods. Another difference between living now and in the past is the fact that nowadays the access to education is much easier. In the past, there were fewer schools and the quality of the education they provided was much poorer. Besides, nowadays it is much easier to find the educational materials that you need thanks to the Internet. Another difference is that in the past there were not that many sources of entertainment. Therefore people used to spend more time with their friends. All things considering, it is an obvious conclusion that life has changed significantly in the last couple of years. It is definitely much easier now however, it is not easy to decide whether it is also better. The scientific achievements and technological progress are amazing. We still create new inventions which simplify our lives. On the other hand, however, life now is much faster and busier than it used to be in the past. Moreover, it has also become more dangerous. The standard of living has definitely improved but it can be discussed whether the quality of our lives is also better. How to cite Life in the Past and Life Now-Comparison., Essay examples Life in the Past and Life Now-Comparison. Free Essays Life in the past and life now-comparison. Over the last century, there have been many significant changes in the way we live. Obviously, it is hard to compare the life of the ancient people and the life of the people of the twenty first century because so many changes have occurred. We will write a custom essay sample on Life in the Past and Life Now-Comparison. or any similar topic only for you Order Now However, even the changes that have taken place over the last ten years are amazing. To start with, people were not able to travel such long distances in such short period of time. The planes were not that popular then because they used to be a very expensive means of transport. Nowadays, we have become very mobile. We have fast and comfortable cars and more and more people also travel by plane. Moreover, in the past people had to work much harder as they did not have tools which made their work easier. Today, most of the difficult and dangerous work is done by computers and other machines. In the past the conditions of living were not as comfortable as they are now. There were not bathrooms and running water in many houses, besides many people could not afford household appliances like fridge, TV set or vacuum cleaner because they used to be luxurious goods. Another difference between living now and in the past is the fact that nowadays the access to education is much easier. In the past, there were fewer schools and the quality of the education they provided was much poorer. Besides, nowadays it is much easier to find the educational materials that you need thanks to the Internet. Another difference is that in the past there were not that many sources of entertainment. Therefore people used to spend more time with their friends. All things considering, it is an obvious conclusion that life has changed significantly in the last couple of years. It is definitely much easier now however, it is not easy to decide whether it is also better. The scientific achievements and technological progress are amazing. We still create new inventions which simplify our lives. On the other hand, however, life now is much faster and busier than it used to be in the past. Moreover, it has also become more dangerous. The standard of living has definitely improved but it can be discussed whether the quality of our lives is also better. How to cite Life in the Past and Life Now-Comparison., Essays

Population Essays (600 words) - Smog, Ghana, Human Overpopulation

Population Brandon Cox Population Facts Some of the worlds problems are all over the world. One is in Los Angles it is sent in the desert and the water has to be pumped in from the Colorado River and dried up a large lake that only a certain kind of wheat grew. In Ghana, West Africa the problems it faces are extreme high mortality rate. The migrant people celebrate when a family has l0 children half of population is under l6. Bangladesh is a prime example of over population it is the most densely populated country in the world in the l60 million people in a place the size of Wisconsin. Mexico City is the largest capital in the world. l0% of energy in Mexico goes to getting water to Mexico City. Breathing the air for one day is the same thing as smoking a pack of cigarettes. That is how bad the air is. Chattanooga once the smog there was as bad as the smog in L.A. The smog was from the coal power plants. In response they built a downtown park system with trees and the centerpiece of it is the worlds largest freshwater aquarium. The problems in Los Angles are the immigrant population will soon out number the American population. Once the scowls were top of the line but now they are overcrowded old and rundown. Ghana West Africa, some of the problems in Ghana are that the mortality rate is extremely high and having many children is considered good. The families with l0 children over 7 years old are celebrated and thought very high of in the village. The main problem in Bangladesh is l60 million people living in an area the the of Wisconsin. Where that many people are living in such a small area there are will be problems. Mexico's problem is that the people in the countries come to Mexico City in hope of a better life. Half of population is under l8 people dig through garbage and trash daily. The problems facing Chattanooga are smog from the coal power plants they soon realized that what they were doing reacted and cleaned up the city. In the Los Angles the illegal immigrants are over crowding the schools the should check green cards and people without them should be sent home. Here is one thing I don't agree with, I a husband and wife come into the United States from Mexico illegally and the wife has a baby in the U.S. then the baby is a citizen ,this is not right. parents should be U.S. citizens but there is not much you can do about this it is in the United States Constitution and it would be hard to change. Ghana the birth rate is tremendous and half of the population is under l5. One way to solve the problem. One way to solve the problem I know is for the U.S. to cut all aid to that country let the people see that they are going to do something for themselves and not rely on other countries for support. Bangladesh is faced with the same problem Ghana, girls l5 years of age already have 2 children.Some people are trying to control births and educate the women not to have as many children. In Mexico City they waste too much energy getting water to the capital and the trash problems are bad they need to rethink their political system and try to more money to help with the problems within Mexico. Chattanooga right now doesn't have any problems right now. They cleaned up their act by getting rid of the coal plants and promotes the green leaf project and works with the mountains and are saving 300 acres from being destroyed for condos. Current Events